The primary constraint on AI scaling isn't just semiconductor fabrication capacity. It's a series of dependent bottlenecks, from TSMC's fabs to the limited number of EUV machines from ASML, and even further down to ASML's own specialized suppliers for components like lenses and glass.
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Specialized AI cloud providers like CoreWeave face a unique business reality where customer demand is robust and assured for the near future. Their primary business challenge and gating factor is not sales or marketing, but their ability to secure the physical supply of high-demand GPUs and other AI chips to service that demand.
Analyst Chris Miller argues China's core challenge is manufacturing, as it lacks the advanced lithography tools monopolized by ASML. The US and Taiwan are projected to produce 30 times more quality-adjusted AI chips, a gap unlikely to close soon.
Despite huge demand for AI chips, TSMC's conservative CapEx strategy, driven by fear of a demand downturn, is creating a critical silicon supply shortage. This is causing AI companies to forego immediate revenue.
The AI industry's growth constraint is a swinging pendulum. While power and data center space are the current bottlenecks (2024-25), the energy supply chain is diverse. By 2027, the bottleneck will revert to semiconductor manufacturing, as leading-edge fab capacity (e.g., TSMC, HBM memory) is highly concentrated and takes years to expand.
While energy supply is a concern, the primary constraint for the AI buildout may be semiconductor fabrication. TSMC, the leading manufacturer, is hesitant to build new fabs to meet the massive demand from hyperscalers, creating a significant bottleneck that could slow down the entire industry.
The critical constraint on AI and future computing is not energy consumption but access to leading-edge semiconductor fabrication capacity. With data centers already consuming over 50% of advanced fab output, consumer hardware like gaming PCs will be priced out, accelerating a fundamental shift where personal devices become mere terminals for cloud-based workloads.
The 2024-2026 AI bottleneck is power and data centers, but the energy industry is adapting with diverse solutions. By 2027, the constraint will revert to semiconductor manufacturing, as leading-edge fab capacity is highly concentrated and takes years to expand.
While energy is a concern, the highly consolidated semiconductor supply chain, with TSMC controlling 90% of advanced nodes and relying on a single EUV machine supplier (ASML), creates a more immediate and inelastic bottleneck for AI hardware expansion than energy production.
Despite record capital spending, TSMC's new facilities won't alleviate current AI chip supply constraints. This massive investment is for future demand (2027-2028 and beyond), forcing the company to optimize existing factories for short-term needs, highlighting the industry's long lead times.
Ben Thompson argues that while investing in unproven fabs from Intel or Samsung seems risky, the greater risk is the entire AI industry being constrained by TSMC's singular capacity. The future opportunity cost of foregone revenue from this bottleneck far outweighs the expense of building up viable competitors.